Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity

Detalhes bibliográficos
Autor(a) principal: Silva, Flavia Alves da
Data de Publicação: 2021
Outros Autores: Correa, Caio Cezar Guedes, Carvalho, Beatriz Murizini, Viana , Alexandre Pio, Preisigke, Sandra da Costa, Amaral Júnior, Antônio Teixeira do
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/183239
Resumo: Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.
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spelling Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearitySEM methodologytrail crestcorrelationMulticollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2021-01-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/18323910.1590/1678-992X-2019-0081Scientia Agricola; v. 78 n. 2 (2021); e20190081Scientia Agricola; Vol. 78 No. 2 (2021); e20190081Scientia Agricola; Vol. 78 Núm. 2 (2021); e201900811678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/183239/169919Copyright (c) 2021 Scientia Agricolahttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessSilva, Flavia Alves da Correa, Caio Cezar Guedes Carvalho, Beatriz Murizini Viana , Alexandre Pio Preisigke, Sandra da Costa Amaral Júnior, Antônio Teixeira do 2021-03-18T18:32:16Zoai:revistas.usp.br:article/183239Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2021-03-18T18:32:16Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
title Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
spellingShingle Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
Silva, Flavia Alves da
SEM methodology
trail crest
correlation
title_short Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
title_full Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
title_fullStr Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
title_full_unstemmed Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
title_sort Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity
author Silva, Flavia Alves da
author_facet Silva, Flavia Alves da
Correa, Caio Cezar Guedes
Carvalho, Beatriz Murizini
Viana , Alexandre Pio
Preisigke, Sandra da Costa
Amaral Júnior, Antônio Teixeira do
author_role author
author2 Correa, Caio Cezar Guedes
Carvalho, Beatriz Murizini
Viana , Alexandre Pio
Preisigke, Sandra da Costa
Amaral Júnior, Antônio Teixeira do
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Silva, Flavia Alves da
Correa, Caio Cezar Guedes
Carvalho, Beatriz Murizini
Viana , Alexandre Pio
Preisigke, Sandra da Costa
Amaral Júnior, Antônio Teixeira do
dc.subject.por.fl_str_mv SEM methodology
trail crest
correlation
topic SEM methodology
trail crest
correlation
description Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-06
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/sa/article/view/183239
10.1590/1678-992X-2019-0081
url https://www.revistas.usp.br/sa/article/view/183239
identifier_str_mv 10.1590/1678-992X-2019-0081
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/183239/169919
dc.rights.driver.fl_str_mv Copyright (c) 2021 Scientia Agricola
http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Scientia Agricola
http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 78 n. 2 (2021); e20190081
Scientia Agricola; Vol. 78 No. 2 (2021); e20190081
Scientia Agricola; Vol. 78 Núm. 2 (2021); e20190081
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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